Publications

Applied Stochastic Differential Equations

By Simo Särkkä and Arno Solin

Published by Cambridge University Press in 2019.

Stochastic differential equations are differential equations whose solutions are stochastic processes. They exhibit appealing mathematical properties that are useful in modeling uncertainties and noisy phenomena in many disciplines. This book is motivated by applications of stochastic differential equations in target tracking and medical technology and, in particular, their use in methodologies such as filtering, smoothing, parameter estimation, and machine learning. It builds an intuitive hands-on understanding of what stochastic differential equations are all about, but also covers the essentials of Itô calculus, the central theorems in the field, and such approximation schemes as stochastic Runge–Kutta. Greater emphasis is given to solution methods than to analysis of theoretical properties of the equations. The book’s practical approach assumes only prior understanding of ordinary differential equations. The numerous worked examples and end-of-chapter exercises include application-driven derivations and computational assignments. MATLAB/Octave source code is available for download, promoting hands-on work with the methods.

With permission from the publisher, we are providing a PDF version of the book here. This PDF version is made available for personal use. The copyright in all material rests with the authors (Simo Särkkä and Arno Solin). Commercial reproduction is prohibited, except as authorised by the author and publisher.

Frida Marie Viset, Anton Kullberg, Frederiek Wesel, and Arno Solin (2024). Exploiting Hankel–Toeplitz structures for fast computation of kernel precision matrices. Transactions on Machine Learning Research (TMLR).

Manon Kok, Arno Solin, and Thomas B. Schön (2024). Rao-Blackwellized particle smoothing for simultaneous localization and mapping. Data-Centric Engineering (DCE), 5:e15. Cambridge University Press.

Ella Tamir, Martin Trapp, and Arno Solin (2023). Transport with support: Data-conditional diffusion bridges. Transactions on Machine Learning Research (TMLR).

William J. Wilkinson, Simo Särkkä, and Arno Solin (2023). Bayes–Newton methods for approximate Bayesian inference with PSD guarantees. Journal of Machine Learning Research (JMLR), 24(83):1–50.

Gabriel Riutort-Mayol, Paul-Christian Bürkner, Michael R. Andersen, Arno Solin, and Aki Vehtari (2022). Practical Hilbert space approximate Bayesian Gaussian processes for probabilistic programming. Statistics and Computing, 33(1):17. Springer.

Vikas Verma, Kenji Kawaguchi, Alex Lamb, Juho Kannala, Arno Solin, Yoshua Bengio, and David Lopez-Paz (2022). Interpolation consistency training for semi-supervised learning. Neural Networks, 145:90–106. Elsevier.

Arno Solin and Simo Särkkä (2020). Hilbert space methods for reduced-rank Gaussian process regression. Statistics and Computing, 30(2):419–446.

James Hensman, Nicolas Durrande, and Arno Solin (2018). Variational Fourier features for Gaussian processes. Journal of Machine Learning Research (JMLR), 18(151):1–52.

Arno Solin, Manon Kok, Niklas Wahlström, Thomas B. Schön, and Simo Särkkä (2018). Modeling and interpolation of the ambient magnetic field by Gaussian processes. IEEE Transactions on Robotics (T-RO), 34(4):1112–1127.

Juho Kokkala, Arno Solin, and Simo Särkkä (2016). Sigma-point filtering and smoothing based parameter estimation in nonlinear dynamic systems. Journal of Advances in Information Fusion, 11(1):15–30.

Arno Solin and Simo Särkkä (2013). Infinite-dimensional Bayesian filtering for detection of quasiperiodic phenomena in spatiotemporal data. Physical Review E, 88(5):052909.

Simo Särkkä, Arno Solin, and Jouni Hartikainen (2013). Spatiotemporal learning via infinite-dimensional Bayesian filtering and smoothing. IEEE Signal Processing Magazine, 30(4):51–61. IEEE.

Simo Särkkä, Arno Solin, Aapo Nummenmaa, Aki Vehtari, Toni Auranen, Simo Vanni, and Fa-Hsuan Lin (2013). Dynamic retrospective filtering of physiological noise in BOLD fMRI: DRIFTER. NeuroImage, 60(2):1517–1527.

Rui Li, Martin Trapp, Marcus Klasson, and Arno Solin (2025). Flatness improves backbone generalisation in few-shot classification. Accepted for publication in IEEE Winter Conference on Applications of Computer Vision (WACV).

Oliver Hamelijnck, Arno Solin, and Theodoros Damoulas (2024). Physics-informed variational state-space Gaussian processes. Accepted for publication in Advances in Neural Information Processing Systems (NeurIPS).

Manon Kok and Arno Solin (2024). Online one-dimensional magnetic field SLAM with loop-closure detection. Accepted for publication in International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI).

Marcus Klasson, Riccardo Mereu, Juho Kannala, and Arno Solin (2024). Sources of uncertainty in 3D scene reconstruction. Accepted for publication in Proceedings of European Conference on Computer Vision Workshops (ECCVW).

Otto Seiskari, Jerry Ylilammi, Valtteri Kaatrasalo, Pekka Rantalankila, Matias Turkulainen, Juho Kannala, Esa Rahtu, and Arno Solin (2024). Gaussian splatting on the move: Blur and rolling shutter compensation for natural camera motion. Accepted for publication in Proceedings of European Conference on Computer Vision (ECCV).

Aidan Scannell, Riccardo Mereu, Paul E. Chang, Ella Tamir, Joni Pajarinen, and Arno Solin (2024). Function-space parameterization of neural networks for sequential learning. In International Conference on Learning Representations (ICLR). Vienna, Austria.

Lorenzo Loconte, Aleksanteri Sladek, Stefan Mengel, Martin Trapp, Arno Solin, Nicolas Gillis, and Antonio Vergari (2024). Subtractive mixture models via squaring: Representation and learning. In International Conference on Learning Representations (ICLR). Vienna, Austria. Accepted as spotlight (top 5%)

Prakhar Verma, Vincent Adam, and Arno Solin (2024). Variational Gaussian process diffusion processes. In Proceedings of the 27th International Conference on Artificial Intelligence and Statistics (AISTATS). PMLR 238:1909-1917.

Lassi Meronen, Martin Trapp, Andrea Pilzer, Le Yang, and Arno Solin (2024). Fixing overconfidence in dynamic neural networks. In IEEE Winter Conference on Applications of Computer Vision (WACV).

Severi Rissanen, Markus Heinonen, and Arno Solin (2023). Improving discrete diffusion models via structured preferential generation. In NeurIPS 2023 Workshop on Diffusion Models.

Xuanlong Yu, Yi Zuo, Zitao Wang, Xiaowen Zhang, Jiaxuan Zhao, Yuting Yang, Licheng Jiao, Rui Peng, Xinyi Wang, Junpei Zhang, Kexin Zhang, Fang Liu, Roberto Alcover-Couso, Juan C SanMiguel, Marcos Escudero-Viñolo, Hanlin Tian, Kenta Matsui, Tianhao Wang, Fahmy Adan, Zhitong Gao, Xuming He, Quentin Bouniot, Hossein Moghaddam, Shyam Nandan Rai, Fabio Cermelli, Carlo Masone, Andrea Pilzer, Elisa Ricci, Andrei Bursuc, Arno Solin, Martin Trapp, Rui Li, Angela Yao, Wenlong Chen, Ivor Simpson, Neill DF Campbell, and Gianni Franchi (2023). The robust semantic segmentation UNCV2023 challenge results. In Proceedings of the IEEE/CVF International Conference on Computer Vision Workshops (ICCVW).

Aleksanteri Sladek, Martin Trapp, and Arno Solin (2023). Encoding negative dependencies in probabilistic circuits. In The 6th Workshop on Tractable Probabilistic Modeling.

Paul Edmund Chang, Prakhar Verma, S.T. John, Arno Solin, and Mohammad Emtiyaz Khan (2023). Memory-based dual Gaussian processes for sequential learning. In Proceedings of the 40th International Conference on Machine Learning (ICML). PMLR 202:4035–4054. Accepted as oral presentation.

Rui Li, S.T. John, and Arno Solin (2023). Improving hyperparameter learning under approximate inference in Gaussian process models. In Proceedings of the 40th International Conference on Machine Learning (ICML). PMLR 202:19595–19615.

Dharmesh Tailor, Paul E. Chang, Siddharth Swaroop, Eric Nalisnick, Arno Solin, and Mohammad Emtiyaz Khan (2023). Memory Maps to Understand Models. In ICML 2023 Workshop on Duality for Modern Machine Learning. Honolulu, Hawaii, USA.

Aidan Scannell, Riccardo Mereu, Paul E. Chang, Ella Tamir, Joni Pajarinen, and Arno Solin (2023). Sparse Function-space Representation of Neural Networks. In ICML 2023 Workshop on Duality for Modern Machine Learning. Honolulu, Hawaii, USA.

Severi Rissanen, Markus Heinonen, and Arno Solin (2023). Generative modelling with inverse heat dissipation. In International Conference on Learning Representations (ICLR).

Andrea Pilzer, Yuxin Hou, Niki Loppi, Arno Solin, and Juho Kannala (2023). Expansion of visual hints for improved generalization in stereo matching. In IEEE Winter Conference on Applications of Computer Vision (WACV).

Prakhar Verma, Paul E. Chang, Arno Solin, and Mohammad Emtiyaz Khan (2022). Sequential learning in GPs with memory and Bayesian leverage score. In Continual Lifelong Learning Workshop (ACML 2022). Hyderabad, India.

Paul E. Chang, Prakhar Verma, ST John, Victor Picheny, Henry Moss, and Arno Solin (2022). Fantasizing with dual GPs in Bayesian optimization and active learning. In NeurIPS Workshop on Gaussian Processes, Spatiotemporal Modeling, and Decision-making Systems. New Orleans, LA, US.

Rui Li, ST John, and Arno Solin (2022). Towards improved learning in Gaussian processes: The best of two worlds. In NeurIPS Workshop on Gaussian Processes, Spatiotemporal Modeling, and Decision-making Systems. New Orleans, LA, US.

Subhankar Roy, Martin Trapp, Andrea Pilzer, Juho Kannala, Nicu Sebe, Elisa Ricci, and Arno Solin (2022). Uncertainty-guided source-free domain adaptation. In Proceedings of European Conference on Computer Vision (ECCV). Lecture Notes in Computer Science 13685:537–555. Tel Aviv, Israel. Springer Nature.

Martin Trapp and Arno Solin (2022). On priors in Bayesian probabilistic circuits and multivariate Pólya trees. In The 5th Workshop on Tractable Probabilistic Modeling.

Martin Trapp, Steven Lang, Aastha Shah, Martin Mundt, Kristian Kersting, and Arno Solin (2022). Towards coreset learning in probabilistic circuits. In The 5th Workshop on Tractable Probabilistic Modeling.

Arno Solin, Rui Li, and Andrea Pilzer (2022). A look at improving robustness in visual-inertial SLAM by moment matching. In Proceedings of the International Conference on Information Fusion (FUSION).

Alexander Nikitin, ST John, Arno Solin, and Samuel Kaski (2022). Non-separable spatio-temporal graph kernels via SPDEs. In Proceedings of the 25th International Conference on Artificial Intelligence and Statistics (AISTATS). PMLR 151:10640–10660.

Otto Seiskari, Pekka Rantalankila, Juho Kannala, Jerry Ylilammi, Esa Rahtu, and Arno Solin (2022). HybVIO: Pushing the limits of real-time visual-inertial odometry. In IEEE Winter Conference on Applications of Computer Vision (WACV). Pages 287–296.

Arno Solin, Ella Tamir, and Prakhar Verma (2021). Scalable inference in SDEs by direct matching of the Fokker–Planck–Kolmogorov equation. In Advances in Neural Information Processing Systems 34 (NeurIPS). Pages 417–429. Curran Associates, Inc.

Vincent Adam, Paul E. Chang, Mohammad Emtiyaz Khan, and Arno Solin (2021). Dual parameterization of sparse variational Gaussian processes. In Advances in Neural Information Processing Systems 34 (NeurIPS). Pages 11474–11486. Curran Associates, Inc.

Lassi Meronen, Martin Trapp, and Arno Solin (2021). Periodic activation functions induce stationarity. In Advances in Neural Information Processing Systems 34 (NeurIPS). Pages 1673–1685. Curran Associates, Inc.

Oliver Hamelijnck, William J. Wilkinson, Niki Andreas Loppi, Arno Solin, and Theo Damoulas (2021). Spatio-temporal variational Gaussian processes. In Advances in Neural Information Processing Systems 34 (NeurIPS). Pages 23621–23633. Curran Associates, Inc.

Prakhar Verma, Vincent Adam, and Arno Solin (2021). Sparse Gaussian processes for stochastic differential equations. In The Symbiosis of Deep Learning and Differential Equations (NeurIPS 2021 Workshop).

Will Tebbutt, Arno Solin, and Richard E. Turner (2021). Combining pseudo-point and state space approximations for sum-separable Gaussian processes. In Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence (UAI). PMLR 161:1607–1617.

William J. Wilkinson, Arno Solin, and Vincent Adam (2021). Sparse algorithms for Markovian Gaussian processes. In Proceedings of the 24th International Conference on Artificial Intelligence and Statistics (AISTATS). PMLR 130:1747–1755.

Will Tebbutt, Arno Solin, and Richard E. Turner (2021). Combining pseudo-point and state space approximations for sum-separable Gaussian processes. In 3rd Symposium on Advances in Approximate Bayesian Inference (AABI).

Yuxin Hou, Ari Heljakka, and Arno Solin (2021). Gaussian process priors for view-aware inference. In Thirty-fifth AAAI Conference on Artificial Intelligence (AAAI). 35(9):7762–7770. AAAI Press.

Yuxin Hou, Arno Solin, and Juho Kannala (2021). Novel view synthesis via depth-guided skip connections. In IEEE Winter Conference on Applications of Computer Vision (WACV). Pages 3118–3127. Waikoloa, HI, USA. IEEE.

Lassi Meronen, Christabella Irwanto, and Arno Solin (2020). Stationary activations for uncertainty calibration in deep learning. In Advances in Neural Information Processing Systems 33 (NeurIPS). Pages 2338–2350. Curran Associates, Inc.

Ari Heljakka, Yuxin Hou, Juho Kannala, and Arno Solin (2020). Deep automodulators. In Advances in Neural Information Processing Systems 33 (NeurIPS). Pages 13702–13713. Curran Associates, Inc.

Yuxin Hou, Muhammad Kamran Janjua, Juho Kannala, and Arno Solin (2020). Movement-induced priors for deep stereo. In International Conference on Pattern Recognition (ICPR).

William J. Wilkinson, Paul E. Chang, Michael Riis Andersen, and Arno Solin (2020). State space expectation propagation: Efficient inference schemes for temporal Gaussian processes. In Proceedings of the 37th International Conference on Machine Learning (ICML). PMLR 119:10270-10281.

Wessel P. Bruinsma, Eric Perim, Will Tebbutt, J. Scott Hosking, Arno Solin, and Richard E. Turner (2020). Scalable exact inference in multi-output Gaussian processes. In Proceedings of the 37th International Conference on Machine Learning (ICML). PMLR 119:1190-1201.

Paul E. Chang, William J. Wilkinson, Mohammad Emtiyaz Khan, and Arno Solin (2020). Fast variational learning in state-space Gaussian process models. In IEEE International Workshop on Machine Learning for Signal Processing (MLSP).

Lassi Meronen, William J. Wilkinson, and Arno Solin (2020). Movement tracking by optical flow assisted inertial navigation. In Proceedings of the International Conference on Information Fusion (FUSION).

Ari Heljakka, Arno Solin, and Juho Kannala (2020). Towards photographic image manipulation with balanced growing of generative autoencoders. In IEEE Winter Conference on Applications of Computer Vision (WACV). Aspen, CO, USA.

William J. Wilkinson, Paul E. Chang, Michael Riis Andersen, and Arno Solin (2019). Global approximate inference via local linearisation for temporal Gaussian processes. In 2nd Symposium on Advances in Approximate Bayesian Inference (AABI). Vancouver, Canada.

Ari Heljakka, Yuxin Hou, Juho Kannala, and Arno Solin (2019). Conditional image sampling by deep automodulators. In Bayesian Deep Learning Workshop, NeurIPS 2019 Workshop. Vancouver, Canada.

Yuxin Hou, Juho Kannala, and Arno Solin (2019). Multi-view stereo by temporal nonparametric fusion. In International Conference on Computer Vision (ICCV). Pages 2651–2660. Seoul, Korea.

Santiago Cortés, Yuxin Hou, Juho Kannala, and Arno Solin (2019). Iterative path reconstruction for large-scale inertial navigation on smartphones. In Proceedings of the International Conference on Information Fusion (FUSION). Ottawa, Canada.

William J. Wilkinson, Michael Riis Andersen, Joshua D. Reiss, Dan Stowell, and Arno Solin (2019). End-to-End Probabilistic Inference for Nonstationary Audio Analysis. In Proceedings of the 36th International Conference on Machine Learning (ICML). PMLR 97:6776–6785. Long Beach, California, USA.

Yuxin Hou, Arno Solin, and Juho Kannala (2019). Unstructured multi-view depth estimation using mask-based multiplane representation. In Scandinavian Conference on Image Analysis (SCIA). Norrköping, Sweden.

William J. Wilkinson, Michael Riis Andersen, Joshua D. Reiss, Dan Stowell, and Arno Solin (2019). Unifying probabilistic models for time-frequency analysis. In International Conference on Acoustics, Speech and Signal Processing (ICASSP). Pages 3352-3356. Brighton, UK.

Arno Solin and Manon Kok (2019). Know your boundaries: Constraining Gaussian processes by variational harmonic features. In Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics (AISTATS). PMLR 89:2193–2202. Naha, Okinawa, Japan.

Arno Solin, James Hensman, and Richard E. Turner (2018). Infinite-horizon Gaussian processes. In Advances in Neural Information Processing Systems 31 (NeurIPS). Pages 3490–3499. Montréal, Canada. Curran Associates, Inc.

Ari Heljakka, Arno Solin, and Juho Kannala (2018). Pioneer networks: Progressively growing generative autoencoder. In Asian Conference on Computer Vision (ACCV). Lecture Notes in Computer Science 11361:22–38. Perth, Australia. Springer.

Santiago Cortés, Arno Solin, and Juho Kannala (2018). Deep learning based speed estimation for constraining strapdown inertial navigation on smartphones. In IEEE International Workshop on Machine Learning for Signal Processing (MLSP). Aalborg, Denmark.

Ari Heljakka, Arno Solin, and Juho Kannala (2018). Recursive chaining of reversible image-to-image translators for face aging. In Proceedings of Advanced Concepts for Intelligent Vision Systems (ACIVS). Lecture Notes in Computer Science 11182:309–320. Poitiers, France. Springer.

Santiago Cortés, Arno Solin, Esa Rahtu, and Juho Kannala (2018). ADVIO: An authentic dataset for visual-inertial odometry. In Proceedings of European Conference on Computer Vision (ECCV). Lecture Notes in Computer Science 11214:425–440. Munich, Germany. Springer.

Hannes Nickisch, Arno Solin, and Alexander Grigorievskiy (2018). State Space Gaussian Processes with Non-Gaussian Likelihood. In International Conference on Machine Learning (ICML). PMLR 80:3789–3798. Stockholm, Sweden.

Arno Solin, Santiago Cortés, Esa Rahtu, and Juho Kannala (2018). Inertial Odometry on Handheld Smartphones. In Proceedings of the International Conference on Information Fusion (FUSION). Pages 1361–1368. Cambridge, UK.

Santiago Cortés, Arno Solin, and Juho Kannala (2018). Robust Gyroscope-Aided Camera Self-Calibration. In Proceedings of the International Conference on Information Fusion (FUSION). Pages 772–779. Cambridge, UK.

Arno Solin, Santiago Cortés, Esa Rahtu, and Juho Kannala (2018). PIVO: Probabilistic inertial-visual odometry for occlusion-robust navigation. In IEEE Winter Conference on Applications of Computer Vision (WACV). Pages 616–625. Lake Tahoe, NV, USA.

Toni Karvonen, Arno Solin, Ángel F. García-Fernández, Filip Tronarp, Simo Särkkä, and Fa-Hsuan Lin (2017). Where is physiological noise lurking in k-space?. In Proceedings of ISMRM 2017, 25th Annual Meeting & Exhibition. Honolulu, HI, USA. Number 5300. The International Society for Magnetic Resonance in Medicine. Appeared as abstract and e-poster.

Arno Solin, Simo Särkkä, Juho Kannala, and Esa Rahtu (2016). Terrain navigation in the magnetic landscape: Particle filtering for indoor positioning. In Proceedings of the European Navigation Conference (ENC). Pages 1–9. IEEE.

Andreas Svensson, Arno Solin, Simo Särkkä, and Thomas B. Schön (2016). Computationally efficient Bayesian learning of Gaussian process state space models. In Proceedings of the Nineteenth International Conference on Artifcial Intelligence and Statistics (AISTATS). JMLR W&CP 51:213–221. Cadiz, Spain.

Andreas Svensson, Thomas B. Schön, Arno Solin, and Simo Särkkä (2015). Nonlinear state space model identification using a regularized basis function expansion. In Proceedings of the 6th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP). Pages 481–484. IEEE.

Eric Malmi, Arno Solin, and Aristides Gionis (2015). The blind leading the blind: Network-based location estimation under uncertainty. In Machine Learning and Knowledge Discovery in Databases (ECML PKDD). Lecture Notes in Computer Science 9285:406–421. Springer.

Eric Malmi, Arno Solin, and Aristides Gionis (2015). Reconstructing and analyzing family trees: Towards longitudinal computational social science. In International Conference on Computational Social Science (ICCSS). Appeared as abstract and poster.

Arno Solin and Simo Särkkä (2015). State space methods for efficient inference in Student-t process regression. In Proceedings of the Eighteenth International Conference on Artifcial Intelligence and Statistics (AISTATS). JMLR W&CP 38:885–893. San Diego, US.

Arno Solin and Simo Särkkä (2014). Gaussian quadratures for state space approximation of scale mixtures of squared exponential covariance functions. In Proceedings of the IEEE International Workshop on Machine Learning for Signal Processing (MLSP). Reims, France.

Juho Kokkala, Arno Solin, and Simo Särkkä (2014). Expectation maximization based parameter estimation by sigma-point and particle smoothing. In Proceedings of the 17th International Conference on Information Fusion (FUSION). Salamanca, Spain.

Arno Solin and Simo Särkkä (2014). Explicit link between periodic covariance functions and state space models. In Proceedings of the Seventeenth International Conference on Artifcial Intelligence and Statistics (AISTATS). JMLR W&CP 33:904–912. Reykjavik, Iceland.

Arno Solin, Simo Särkkä, Aapo Nummenmaa, Aki Vehtari, Toni Auranen, and Fa-Hsuan Lin (2014). Catching physiological noise: Comparison of DRIFTER in image and k-Space. In Proceedings of ISMRM 2014, 22nd Annual Meeting & Exhibition. Milan, Italy. The International Society for Magnetic Resonance in Medicine. Number 3068. Appeared as abstract and poster.

Simo Särkkä and Arno Solin (2013). Continuous-space Gaussian process regression and generalized Wiener filtering with application to learning curves. In Scandinavian Conference on Image Analysis (SCIA). Lecture Notes in Computer Science 7944:172–181. Springer.

Arno Solin, Enrico Glerean, and Simo Särkkä (2013). Time–frequency dynamics of brain connectivity by stochastic oscillator models and Kalman filtering. In The 19th Annual Meeting of the Organization for Human Brain Mapping. Seattle, WA, USA. Number 1877. Appeared as abstract and poster.

Arno Solin, Simo Särkkä, Aapo Nummenmaa, Aki Vehtari, Toni Auranen, Simo Vanni, and Fa-Hsuan Lin (2013). Volumetric space-time structure of physiological noise in BOLD fMRI. In Proceedings of ISMRM 2013, 21st Annual Meeting & Exhibition. Salt Lake City, UT, US. The International Society for Magnetic Resonance in Medicine. Number 3353. Appeared as abstract and e-poster.

Simo Särkkä, Arno Solin, Aapo Nummenmaa, Aki Vehtari, Toni Auranen, Simo Vanni, and Fa-Hsuan Lin (2012). Identification of spatio-temporal oscillatory signal structure in cerebral hemodynamics using DRIFTER. In Proceedings of ISMRM 2012, 20th Annual Meeting & Exhibition. Melbourne. The International Society for Magnetic Resonance in Medicine. Number 2842. Appeared as abstract and e-poster.

Simo Särkkä and Arno Solin (2012). On continuous-discrete cubature Kalman filtering. In Proceedings of SYSID 2012, 16th IFAC Symposium on System Identification. Pages 1210–1215. Brussels.

Simo Särkkä, Aapo Nummenmaa, Arno Solin, Aki Vehtari, Thomas Witzel, Toni Auranen, Simo Vanni, Matti S. Hämälainen, and Fa-Hsuan Lin (2011). Dynamical statistical modeling of physiological noise for fast BOLD fMRI. In Proceedings of ISMRM 2011, 19th Annual Meeting & Exhibition. Montreal. The International Society for Magnetic Resonance in Medicine. Number 3592. Appeared as abstract and e-poster.